Approximate robust Optimal Experiment Design in dynamic bioprocess models

In dynamic bioprocess models parameters often appear in a nonlinear way. When designing optimal experiments to calibrate these models, the Fisher Information Matrix explicitly depends on the current parameter estimates. Hence, it is advisable to take this parametric uncertainty into account in the design procedure in order to obtain an experiment which is robust with respect to changes in the parameters. The current paper applies computationally efficient approximate robustification strategies based on a worst case scenario. Both methods exploit linearisation techniques to avoid the hard to solve max-min optimisation problems. The methods will be illustrated on a predictive microbiology case study.